The significant demand for high quality food has motivated us to adopt appropriate processing methods to improve the food nutritional quality and flavors. In this study, the effects of five drying methods, namely, pulsed vacuum drying (PVD), freeze drying (FD), infrared drying (IRD), hot-air drying (HAD) and sun drying (SD) on free amino acids (FAAs), α-dicarbonyl compounds (α-DCs) and volatile compounds (VOCs) in rape bee pollen (RBP) were determined. The results showed that FD significantly released the essential amino acids (EAAs) compared with fresh samples while SD caused the highest loss. Glucosone was the dominant α-DCs in RBP and the highest loss was observed after PVD. Aldehydes were the dominant volatiles of RBP and SD samples contained more new volatile substances (especially aldehydes) than the other four drying methods. Comprehensively, FD and PVD would be potential methods to effectively reduce the quality deterioration of RBP in the drying process.

To shorten the complex and time-consuming process of the identification method of the traditional food angiotensin-I-converting enzyme (ACE-I) inhibitory peptides, we propose AHTPeptideFusion based on a segmented fusion with the protein language model and deep learning. The statistical analysis found that hydrophobic amino acids, N-terminal valine is a dominant amino acid in the activity of ACE-I inhibitory peptides. In 12 machine learning (ML) algorithms, the transformer outperformed the other 11 models, with the best performance in predicting short and medium peptides. In the external dataset, AHTPeptideFusion fused by transformer and random forest (RF) showed excellent performance (accuracy > 0.9) in predicting ACE-I inhibitory peptides with lengths ranging from 2 to 15 amino acid residues and different activity distributions, and the reliability and accuracy of AHTPeptideFusion was demonstrated by synthetic peptide and ACE-I inhibition experiments. In addition, hydrogen bonding and electrostatic interaction between 4 synthetic peptides and active residues of ACE-I were found by molecular docking. To further explore the ACE-I inhibitory peptides from animal-derived foods, we established an automated pipeline consisting of the trinity of proteomics, virtual enzymatic digestion and AHTPeptideFusion, and tapped the ACE-I inhibitory peptide released from royal jelly after digestion in the gastrointestinal tract. In conclusion, this computational pipeline will become a powerful screening tool for active peptides from animal-derived foods, which can help food scientists accelerate the mining and design of active peptides from animal-derived foods. Overall, AHTPeptideFusion will be a powerful ACE-I inhibitor peptide prediction tool, it can help food scientists accelerate the mining and design of ACE-I inhibitory peptides.